FACTS: Fine-Grained Action Classification for Tactical Sports
This addresses the problem of analyzing complex movements in tactical sports for training, performance analysis, and spectator engagement, representing a strong domain-specific advancement.
The paper tackles fine-grained action classification in fast-paced sports like fencing and boxing by introducing FACTS, a transformer-based method that processes raw video directly without pose estimation, achieving 90% accuracy on fencing and 83.25% on boxing.
Classifying fine-grained actions in fast-paced, close-combat sports such as fencing and boxing presents unique challenges due to the complexity, speed, and nuance of movements. Traditional methods reliant on pose estimation or fancy sensor data often struggle to capture these dynamics accurately. We introduce FACTS, a novel transformer-based approach for fine-grained action recognition that processes raw video data directly, eliminating the need for pose estimation and the use of cumbersome body markers and sensors. FACTS achieves state-of-the-art performance, with 90% accuracy on fencing actions and 83.25% on boxing actions. Additionally, we present a new publicly available dataset featuring 8 detailed fencing actions, addressing critical gaps in sports analytics resources. Our findings enhance training, performance analysis, and spectator engagement, setting a new benchmark for action classification in tactical sports.